U.S. patent application number 16/194999 was filed with the patent office on 2020-05-21 for automated prevention of sending objectionable content through electronic communications.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Hao Chen, Ya Bin Dang, Qi Cheng Li, Lijun Mei, Xin Zhou.
Application Number | 20200162412 16/194999 |
Document ID | / |
Family ID | 70728298 |
Filed Date | 2020-05-21 |
United States Patent
Application |
20200162412 |
Kind Code |
A1 |
Mei; Lijun ; et al. |
May 21, 2020 |
AUTOMATED PREVENTION OF SENDING OBJECTIONABLE CONTENT THROUGH
ELECTRONIC COMMUNICATIONS
Abstract
A computer implemented method of pre-emptively blocking an
electronic communication is provided. The computer implemented
method includes inputting an electronic communication history,
wherein the electronic communication history includes a plurality
of electronic communications and a set of corresponding recipients
for each of the plurality of electronic communications. The
computer implemented method further includes normalizing the
plurality of electronic communications, and extracting a topic from
each of the plurality of electronic communications. The computer
implemented method further includes clustering the plurality of
electronic communications according to the extracted topics, and
digesting the plurality of electronic communication to form a
positive learning data set and a negative learning data set to
train a neural network. The computer implemented method further
includes training the neural network on the positive learning data
set and the negative learning data set, and preparing a positive
neutral network model and a negative neural network model.
Inventors: |
Mei; Lijun; (Beijing,
CN) ; Li; Qi Cheng; (Beijing, CN) ; Zhou;
Xin; (Beijing, CN) ; Dang; Ya Bin; (Beijing,
CN) ; Chen; Hao; (Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
70728298 |
Appl. No.: |
16/194999 |
Filed: |
November 19, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 20/00 20190101;
H04L 51/12 20130101; G06N 3/0454 20130101; G06N 3/084 20130101;
G06N 7/005 20130101; G06N 3/088 20130101 |
International
Class: |
H04L 12/58 20060101
H04L012/58; G06N 3/04 20060101 G06N003/04; G06N 3/08 20060101
G06N003/08 |
Claims
1. A computer implemented method of pre-emptively blocking an
electronic communication, comprising: inputting an electronic
communication history, wherein the electronic communication history
includes a plurality of electronic communications and a set of
corresponding recipients for each of the plurality of electronic
communications; normalizing the plurality of electronic
communications; extracting a topic from each of the plurality of
electronic communications; clustering the plurality of electronic
communications according to the extracted topics; digesting the
plurality of electronic communications to form a positive learning
data set and a negative learning data set to train a neural
network; training the neural network on the positive learning data
set and the negative learning data set; and preparing a positive
neutral network model and a negative neural network model.
2. The computer implemented method of claim 1, further comprising
implementing the positively and negatively trained neural network
on a communications platform, wherein the positively and negatively
trained neural network analyzes a newly created electronic message
being sent using the communications platform, and pre-emptively
blocking the newly created electronic message identified as
containing objectionable material based on the set of corresponding
recipients for the newly created electronic message.
3. The computer implemented method of claim 2, wherein the
normalization include removing stop-words, correcting or removing
mis-spellings and typos, and/or enriching the electronic messages
using the context information.
4. The computer implemented method of claim 3, wherein clustering
utilizes expectation maximization (EM) approach, a K-means
approach, or a Gaussian Mixture Model.
5. The computer implemented method of claim 4, wherein content
extraction utilizes key term identification and/or Latent Dirichlet
Allocation (LDA).
6. The computer implemented method of claim 5, wherein the newly
created electronic message includes a text portion, a
graphics/image portion, or both.
7. A processing system, comprising: a central processing unit
(CPU); a graphics processing unit (GPU); a bus electrically
connected to and in electronic communication with the CPU and the
GPU; and a neural network system including: a normalizer configured
to normalize stored electronic communications from a communication
history, pre-existing electronic communications of a message
thread, and/or a newly created electronic message; a content
extractor configured to extract content from the electronic
communications and the electronic message using key term
identification and/or LDA; a cluster generator configured to
generate clusters from the content of the electronic communications
and the electronic message; and a message filterer configured to
filter the electronic communications and the electronic message
with or without attachments using one or more filter matrices.
8. The processing system of claim 7, further comprising a positive
reply generator configured to generate one or more possible
positive replies that are predicted responses.
9. The processing system of claim 8, further comprising a negative
reply generator configured to generate one or more possible
negative replies that are predicted responses.
10. The processing system of claim 9, further comprising a
messaging application run by the CPU.
11. The processing system of claim 10, wherein the cluster
generator utilizes an expectation maximization (EM) approach, a
K-means approach, or a Gaussian Mixture Model.
12. The processing system of claim 11, wherein the processing
system is configured to implement a positively and negatively
trained neural network system on a communications platform, wherein
the positively and negatively trained neural network system
analyzes the newly created electronic message being sent using the
messaging application, and pre-emptively blocks the newly created
electronic message found to contain objectionable material based on
a set of corresponding recipients for the newly created electronic
message.
13. The processing system of claim 12, wherein the newly created
electronic message includes a text portion, a graphics/image
portion, or both.
14. A non-transitory computer readable storage medium comprising a
computer readable program for pre-emptively blocking an electronic
communication, wherein the computer readable program when executed
on a computer causes the computer to perform the steps of:
inputting an electronic communication history, wherein the
electronic communication history includes a plurality of electronic
communications and a set of corresponding recipients for each
electronic communication; normalizing the plurality of electronic
communications; extracting a topic from each of the plurality of
electronic communications; clustering the plurality of electronic
communications according to the extracted topics; digesting the
plurality of electronic communication to form a positive learning
data set and a negative learning data set to train a neural
network; training the neural network on the positive learning data
set and the negative learning data set; and preparing a positive
neutral network model and a negative neural network model.
15. The computer readable program of claim 14, further comprising
implementing the positively and negatively trained neural network
on a communications platform, wherein the positively and negatively
trained neural network analyzes newly created electronic message
being sent using the communications platform, and pre-emptively
blocking the newly created electronic message identified as
containing objectionable material based on the set of corresponding
recipients for the newly created electronic message.
16. The computer readable program of claim 15, wherein the
normalization include removing stop-words, correcting or removing
mis-spellings and typos, and/or enriching the electronic messages
using the context information.
17. The computer readable program of claim 16, wherein clustering
utilizes expectation maximization (EM) approach, a K-means
approach, or a Gaussian Mixture Model.
18. The computer readable program of claim 17, wherein content
extraction utilizes key term identification and/or Latent Dirichlet
Allocation (LDA).
19. The computer readable program of claim 18, wherein the newly
created electronic message includes a text portion, a
graphics/image portion, or both.
20. The computer readable program of claim 19, further comprising
comparing the actual responses received from recipients of the
newly created electronic message to the predicted positive
responses and the predicted negative responses.
Description
BACKGROUND
Technical Field
[0001] The present invention generally relates to neural networks,
and more particularly to neural networks for automated text and
image categorization.
Description of the Related Art
[0002] An artificial neural network (ANN) is an information
processing system that is inspired by biological nervous systems,
such as the brain. The key element of ANNs is the structure of the
information processing system, which includes a large number of
highly interconnected processing elements (called "neurons")
working in parallel to solve specific problems. ANNs are
furthermore trained in-use, with learning that involves adjustments
to weights that exist between the neurons. An ANN can be configured
for a specific application, such as pattern recognition or data
classification, through such a learning process.
SUMMARY
[0003] In accordance with an embodiment of the present invention, a
computer implemented method of pre-emptively blocking an electronic
communication is provided. The computer implemented method includes
inputting an electronic communication history, wherein the
electronic communication history includes a plurality of electronic
communications and a set of corresponding recipients for each of
the plurality of electronic communications. The computer
implemented method further includes normalizing the plurality of
electronic communications, and extracting a topic from each of the
plurality of electronic communications. The computer implemented
method further includes clustering the plurality of electronic
communications according to the extracted topics, and digesting the
plurality of electronic communication to form a positive learning
data set and a negative learning data set to train a neural
network. The computer implemented method further includes training
the neural network on the positive learning data set and the
negative learning data set, and preparing a positive neutral
network model and a negative neural network model.
[0004] In accordance with another embodiment of the present
invention, a processing system is provided. The processing system
includes a central processing unit (CPU), a graphics processing
unit (GPU), and a bus electrically connected to and in electronic
communication with the CPU and the GPU. The processing system
further includes a neural network system including a normalizer
configured to normalize stored electronic communications from a
communication history, pre-existing electronic communications of a
message thread, and/or a newly created electronic message, a
content extractor configured to extract content from the electronic
communications and the electronic message using key term
identification and/or LDA, a cluster generator configured to
generate clusters from the content of the electronic communications
and the electronic message, and a message filterer configured to
filter the electronic communications and the electronic message
with or without attachments using one or more filter matrices.
[0005] In accordance with yet another embodiment of the present
invention, a non-transitory computer readable storage medium
comprising a computer readable program for pre-emptively blocking
an electronic communication is provided. The computer readable
program includes instructions for inputting an electronic
communication history, wherein the electronic communication history
includes a plurality of electronic communications and a set of
corresponding recipients for each electronic communication,
normalizing the plurality of electronic communications, extracting
a topic from each of the plurality of electronic communications,
clustering the plurality of electronic communications according to
the extracted topics, digesting the plurality of electronic
communication to form a positive learning data set and a negative
learning data set to train a neural network, training the neural
network on the positive learning data set and the negative learning
data set, and preparing a positive neutral network model and a
negative neural network model.
[0006] These and other features and advantages will become apparent
from the following detailed description of illustrative embodiments
thereof, which is to be read in connection with the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The following description will provide details of preferred
embodiments with reference to the following figures wherein:
[0008] FIG. 1 is a block/flow diagram showing a training procedure
for a neural network system for preventing or avoiding sending
improper messages or sensitive information to recipients through
electronic communication channels, in accordance with an embodiment
of the present invention;
[0009] FIG. 2 is a block/flow diagram showing a neural network
system implementing embodiments of the present approach to
preventing or avoiding sending improper messages or sensitive
information to receivers through electronic communication channels,
in accordance with an embodiment of the present invention;
[0010] FIG. 3 is an exemplary processing system 300 to which the
present methods and systems may be applied, in accordance with an
embodiment of the present invention.
[0011] FIG. 4 is a block diagram illustratively depicting an
exemplary processing system implementing a neural network for
interrupting objectionable electronic message transmissions, in
accordance with an embodiment of the present invention.
[0012] FIG. 5 is a block diagram illustratively depicting an
exemplary neural network, in accordance with an embodiment of the
present invention;
[0013] FIG. 6 is a block diagram illustratively depicting an
exemplary artificial neural network (ANN) architecture, in
accordance with an embodiment of the present invention;
[0014] FIG. 7 is an illustration of a cloud computing environment,
in accordance with an embodiment of the present invention; and
[0015] FIG. 8 is an illustration of a set of functional abstraction
layers provided by a cloud computing environment 700, as shown in
FIG. 7, in accordance with an embodiment of the present
invention.
DETAILED DESCRIPTION
[0016] Embodiments of the present invention provide a method of
preventing or avoiding sending improper messages or sensitive
information to recipients through electronic communication
channels, for example, using social messaging tools. Unlike
snail-mail and hard-copy memos and letters, the ease with which a
person can disseminate information using electronic communication
channels, for example, e-mail, texting, and chat, to others has
greatly increased the possibility of sending unintended material
and/or sensitive information to multiple receivers. The ability
created by electronic communications to almost instantaneously send
an electronic message to multiple recipients by a single click or
keystroke on a computer device has greatly increased the likelihood
that information, images, or other content can be sent to
unintended or incorrect receivers. Electronic messages also are
difficult, if not impossible, to stop or retrieve once they have
been sent, and unlike hard-copy messages, electronic messages can
be delivered before an issue with the content is even discovered.
Embodiments of the present invention address such problems by
learning the sender's electronic messaging habits, and interceding
when a message appears to be incongruent with the type of
information and message topic(s) that would be sent to the list of
recipients by automatically and pre-emptively blocking the
electronic communication.
[0017] Embodiments of the present invention provide a method of
recognizing when information, images, or other content in an
electronic message should not be sent to one or more recipient
addresses specified in the Addressee field of electronic messages.
By analyzing chat logs, e-mail threads, and communication
histories, a neural network can learn which electronic
communications have been deemed appropriate by both senders and
receivers, and which electronic communications have been deemed
objectionable. The neural network can use such learning to
determine if and when the contents of an electronic message are
inappropriate or unsuited for the individual(s) or groups listed in
the Addressee field of the electronic message. The method can,
thereby, addresses the problems created by computer communication
technology using computer technology based on neural networks.
Automated prevention of sending objectionable content through
electronic communications can avoid embarrassment of the sender or
dissemination of sensitive material.
[0018] Embodiments of the present invention provide a system that
can automatically analyze electronic messages for sensitive
information or inappropriate content before the message(s) are
communicated, and intervene before the information and/or content
is disseminated to the addressees of the electronic message by
pre-emptively blocking the electronic communication. In various
embodiments, not every message being sent is interrupted to ask the
user/sender if the message is acceptable to send. In various
embodiments, the system can determine the likelihood that the
message content is appropriate for each of the listed receivers,
and present a warning to the sender if there is a likelihood that
such content is not appropriate for each and every intended
recipient in the addressee field before transmission of the
electronic message.
[0019] Exemplary applications/uses to which the present invention
can be applied include, but are not limited to: e-mail servers,
instant messaging and chat applications, social messaging tools and
platforms, and other electronic message services that provide
electronic communication capability.
[0020] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0021] Referring now to the drawings in which like numerals
represent the same or similar elements and initially to FIG. 1, a
block/flow diagram showing a training procedure 100 for a neural
network system for preventing or avoiding sending improper messages
or sensitive information to recipients through electronic
communication channels is shown, in accordance with an embodiment
of the present invention.
[0022] In block 110, stored electronic communications, which can be
previously sent electronic messages, including, but not limited to,
e-mail threads, chat logs, and tweet threads, can be input into a
neural network training scheme to train the neural network to
recognize the relationship between acceptable message content
corresponding to a set of recipients and unacceptable content
corresponding to the same recipients. The electronic messages can
include, for example, text portions, graphics and images, numeric
data and information, and/or attachments including text, graphics,
images, numeric data, and other information. Text portions can
include written alpha-numeric elements of a message, such as typed
language entered by a user of an electronic device. Digital
graphics and images can include attached or embedded photographs,
video clips, animated GIFs, drawings, and clip art, where the
graphic(s) or image(s) can be in an acceptable digital format.
Numeric data and information can include tables or lists of numbers
or values, including, but not limited to, social security numbers,
pin numbers, phone numbers, bank account numbers, etc.
[0023] In various embodiments, graphics, images, videos, and
animations can be raster image files or vector image files, where
the graphics and images can be in a format, for example, Joint
Photographic Experts Group (JPEG), Moving Picture Experts Group
(MPEG), flash video (FLV), Audio Video Interleave (AVI), Quick Time
File Format (.mov), AMV video format (.amv), Portable Network
Graphics (PNG), Graphics Interchange Format (GIF), Tagged Image
File Format (TIFF), Photoshop Document (PSD), Portable Document
Format (PDF), Encapsulated Postscript (EPS), Adobe Illustrator
Document (AI), Adobe Indesign Document (INDD), bit map (BMP), or
Raw Image Format (RAW).
[0024] In one or more embodiments, at least a portion of a user's
electronic communications history of electronic messages can be
converted into an input for a neural network, where text
component(s) and digital graphics and image components can be
extracted from the electronic messages, and the addressees
corresponding to each of the corresponding electronic messages can
be identified.
[0025] In block 120, the electronic message inputs can be
normalized and/or scaled, where the components of the electronic
messages may not have a defined range of values, or where the
transformed values can span a wide range. Normalizing the values of
the electronic messages can ensure the values fall within the
domain of the neural network, where the normalization can also
center the data values. In various embodiments, the input of the
electronic messages can be normalized using Min-Max Normalization,
where the input range can be linearly transformed to the interval
[0,1] (or alternatively [-1,1]). In various embodiments, the input
of the electronic messages can be normalized using Z-Score
Normalization, where the data can be transformed to have zero mean
and unit variance. The normalization used can depend on the
approach to backpropagation and/or the activation function(s) used
by the neural network nodes. In addition, other normalization
methods can be applied to the text portion of the electronic
messages, including, but not limited to, removing stop-words from
the text, where stop-words are words which are filtered out because
the words do not contain significant meaning or add significant
meaning to the text. Stop-words can include, for example, articles
(e.g., an, the) prepositions (e.g., about, above, before, if,
etc.), adverbs, (e.g., quickly, often, then, nicely, etc.), or
conjunctions, (e.g., but, and. or, etc.). Embedding vectors can
then be generated after the stop-words have been removed, where
words or phrases from the vocabulary of the text are mapped to
vectors of real numbers.
[0026] In various embodiments, an algorithm, for example, a vector
space model or word2Vec.RTM. using a continuous bag-of-words or
continuous skip-gram can be used to convert the text portion of an
electronic message that is embedded or attached into a vector space
that can be input into the neural network. The vector space of the
text portion of an electronic message can then be normalized.
Embedding vectors can be vectors of real numbers.
[0027] In one or more embodiments, attached and/or embedded digital
graphics and images can be converted into input for the neural
network and normalized if necessary. In various embodiments, an
image may already be within a known range of values, for example,
red-green-blue (RGB) pixel values (e.g., 0-255) or gray-scale
values. The image values may be normalized to an interval of [0,1]
or [-1,1].
[0028] In various embodiments, the input text can be checked for
mis-spellings and typos to help correctly identify the word content
of the electronic messages before the electronic message inputs are
normalized and/or scaled. Unidentifiable words can be dropped from
the electronic messages, and the message flagged as being
incomplete. Incomplete messages can result in a lower confidence
score due to lower level of understanding of the text portion of
the electronic message. Such incomplete messages can be enriched
using the context information from previous conversations.
[0029] In block 130, the topic of the electronic messages can be
extracted by identifying key terms in a subject line of the
electronic message, or multiple recurrences of the same term(s)
with the body and/or attachment(s) of the electronic message. The
key terms can be used to create a catalog of labels. In various
embodiments, Latent Dirichlet Allocation (LDA) can be used for
topic modelling. The LDA can be utilized to extract the main topics
(represented as a set of words) that occur in a collection of the
electronic communications by unsupervised learning. The LDA can
build a topic per document model and words per topic model, modeled
as Dirichlet distributions.
[0030] In various embodiments, the vectors mapping the text can be
clustered using a clustering algorithm, where the clustering
algorithm can be an expectation maximization (EM) approach, a
K-means approach (e.g., using MacQueen's algorithm, Hartigan's
algorithm, etc.), or a Gaussian Mixture Model. After the clusters
are generated, LDA can be used to extract key topics for each
cluster.
[0031] In block 140, the training data for training the neural
network can be prepared from the input electronic communications,
where preparation of the training data can include building
vocabularies of the terms used in the input electronic messages and
categories or labels for the digital images and graphics can be
established. The identification of categories or labels can depend
on the types of images that can be searched for or expected to be
reviewed in determining if the digital images constitute
objectional subject matter, where objectionable subject matter
determination can be based on known ethics, decorum, and/or
etiquette standards (e.g., workplace behavior and harassment
policies, professional ethics rules, criminal laws, etiquette
books, etc.).
[0032] In various embodiments, the text portion(s) and the graphic
and image portions of each of the electronic messages can be
converted into a vector format, where the vectors can be formed by
flattening out the pixels into a single column vector of dimension
[D.times.1], where D is the number of values in a graphic or image.
For example, a 100 pixel by 100 pixel RGB image would include
30,000 values that could form a [30,000.times.1] vector.
[0033] In one or more embodiments, the contacts list of the
user/sender can also be used to correlate message content with
known recipients to determine the appropriateness of the message
content for each recipient of a message. The e-mail address or URL
of the recipient(s) can be used to uniquely identify each recipient
for each electronic communication in the user's electronic
communications history, where an electronic communication includes
an electronic message that has been sent to one or more
addressee(s)/recipients.
[0034] In one or more embodiments, the contact list of the
user/sender can be retrieved from the social messaging tool (e.g.,
e-mail, texting App, etc.). Through correlating the nick names
identified in the communication history with their own contact
list, each message can be labeled with its senders. The e-mail
address or URL of the user/sender may also be extracted for
comparisons.
[0035] In block 150, the neural network can learn positive
outcomes, where the message content was found not to be
objectionable to the recipients. In various embodiments, the
responses of the recipients to each sent message can be used to
determine whether and to what extent the recipients found the
message contents to be objectionable. The text, images, and
attachments of each addressee's reply can be analyzed in a manner
similar to the user's electronic messages to identify positive
responses that reinforce the sending of similar message content
versus identifying negative responses that discourage the sending
of similar message content. Through repeated comparison, the neural
network can learn to predict likely response from known users to
sent message content.
[0036] In block 160, the neural network can learn negative
outcomes, where the message content was found to be objectionable
to at least one of the recipients. In various embodiments, the
responses of the recipients to each sent message can be used to
determine whether and to what extent the recipients found the
message contents to be objectionable. The text, images, and
attachments of each addressee's reply can be analyzed in a manner
similar to the user's sent electronic messages to identify a
negative response that discourages the sending of similar message
content to the particular recipient. Through repeated comparison,
the neural network can learn to predict likely responses from known
addressees to sent message content and calculate a confidence score
regarding the likelihood that the sent message(s) would be found
objectionable.
[0037] In various embodiments, each contact in a user's contact
list can also have associated profile data, where the profile data
can include the relationship that the addressee/contact has with
the sender, for example, whether the addressee/contact is a close
personal friend, an acquaintance, a close relative, a distant
relative, a work associate, a work supervisor, a customer, a
vendor, a client, etc. The user may be requested to input such
profile data to identify the type of relationship each
addressee/contact has with the sender/user, or the system may learn
the relationship type from other information, such as the e-mail
address (e.g., work e-mail address, public e-mail address, etc.),
conversation topics (e.g., family birthdays, weddings, work
assignments, etc.) or categories of attached images (e.g., sports
cars or horses relating to a hobby, family photographs, work or
technical images, etc.). In various embodiments, each
addressee/contact may be associated with more than one relationship
type, where a work associate may also be a close or distant
relative or a close personal friend, etc.
[0038] In block 170, a learning algorithm can be applied to the
normalized input data to train the neural network on positive
outcomes. Training can involve determining or updating the weights
of a positive outcome matrix, M.sub.P, to be applied to a new
electronic message. Training the neural network can produce a
trained positive neural network model. The matrix, M.sub.P, can be
used as a filter to calculate a confidence value that the message
is acceptable or objectionable to the addressee(s)/contact(s)
identified in the message address field.
[0039] In block 180, a learning algorithm can be applied to the
normalized input data to train the neural network on negative
outcomes. Training can involve determining or updating the weights
of a negative outcome matrix, M.sub.N, to be applied to a new
electronic message. Training the neural network can produce a
trained negative neural network model. The matrix, M.sub.N, can be
used as a filter to calculate a confidence value that the message
is acceptable or objectionable to the addressee(s)/contact(s)
identified in the message address field.
[0040] FIG. 2 is a block/flow diagram showing a method 200 of a
neural network system implementing embodiments of the present
approach to preventing or avoiding sending improper messages or
sensitive information to receivers through electronic communication
channels, in accordance with an embodiment of the present
invention.
[0041] In block 210, the most current electronic message generated
by the user can be input into the neural network. The most current
electronic message can be the first electronic message in a new
electronic communication thread, or the most recent electronic
message in an ongoing electronic communication thread, where the
newly created electronic message has not been transmitted to the
recipients listed in the Addressee field.
[0042] In block 220, the newly created electronic message input
into the neural network can be normalized and/or scaled in a manner
as described for the training data used to train the neural
network.
[0043] In block 230, the pre-existing electronic communications of
an ongoing electronic communication thread can be input into the
neural network. Each of one or more pre-existing electronic
messages of the ongoing electronic communication thread can be
input at the time of the user generating a new electronic message,
where the user/sender is engaging in electronic communications with
one or more recipients. The one or more pre-existing electronic
messages of the current electronic communications thread can be
input into a trained neural network system.
[0044] In block 240, each of the one or more pre-existing
electronic messages of the ongoing electronic communication thread
can be input into the neural network in a manner similar to the
training data used to train the neural network. The electronic
message inputs can be normalized and/or scaled in a manner similar
to the training data used to train the neural network.
[0045] In block 250, the topic and content of each of the one or
more pre-existing electronic messages can be extracted in a manner
similar to the training data used to train the neural network.
[0046] In various embodiments, the addressee(s)/contact(s)
identified in the message address field can be extracted from each
of the one or more pre-existing electronic messages and the profile
of the identified addressee(s)/contact(s) can be retrieved from the
social messaging tool.
[0047] In block 260, a message filter can be applied to the newly
created electronic message, where the message filter can identify
whether the newly created electronic message is acceptable for all
listed recipients or unacceptable for any of the listed recipients.
A newly created electronic message that is identified as
unacceptable for at least one of the listed recipients can trigger
an alert to the sender that the electronic message should not be
sent. The neural network can automatically interrupt the
transmission of the electronic message based on the message content
to prevent embarrassment or worse.
[0048] In various embodiments, the message filter can be a simple
threshold filter that searches the newly created electronic message
for black listed words or images, where a matrix calculation is not
used to make the determination that the newly created electronic
message is acceptable for all listed recipients or unacceptable for
any of the listed recipients. In various embodiments, the message
filter can determine that further processing is needed through
checking whether the messaging is in a white list or a black list.
If either a white list or a black list does not contain the
message, then further processing is needed and the neural network
can implement generating a confidence score for the newly created
electronic message. The newly created message can be analyzed using
the trained neural network to determine the probability (e.g.,
confidence score) that the newly created electronic message
contains objectionable content for the listed recipients.
[0049] In one or more embodiments, the message filter can be a
matrix, F, that includes weights applied to the vectors generated
for the newly created electronic message and any attachments, that
categorizes the newly created electronic message as acceptable for
all listed recipients or unacceptable for at least one of the
listed recipients. A message filter can be matrix M.sub.N or
M.sub.P.
[0050] One or more filter matrices can be applied to the different
portions of the newly created electronic message to determine if a
text portion, a graphics/image portion, and/or an attachment
portion contains objectionable material based on the list of
recipients in the addressee field of the newly created electronic
message. In various embodiments, the filters of a trained
convolutional neural network can be applied to each of the
different portions of the newly created electronic message to
analyze the text content and the graphics/image content.
[0051] In block 270, the neural network can generate possible
positive replies that are predicted responses from the listed
recipients of the newly created electronic message, where the
predicted responses are used for comparison with the newly created
electronic message to see whether such message qualifies as a
positive reply (i.e, not objectionable for all addresses). The
neural network can generate a confidence score that evaluates the
likelihood that the newly created electronic message will be
acceptable for all listed recipients.
[0052] In block 280, the neural network can generate possible
negative replies that are predicted responses from the listed
recipients of the newly created electronic message, where the
predicted responses are used for comparison with the newly created
electronic message to see whether such message qualifies as a
negative reply (i.e., objectionable for at least one of the
addressees). The neural network can generate a confidence score
that evaluates the likelihood that the newly created electronic
message will be unacceptable for at least one of the listed
recipients.
[0053] In block 290, actual responses received from the recipients
for the newly created electronic message can be compared to the
predicted positive responses and the predicted negative responses.
Differences between the predicted responses and the actual
responses received from the recipients can be used to update the
message filter(s). The message filter can be updated by changing
the weights of the filter matrix, F, where the message filter can
be updated through back propagation.
[0054] In various embodiments, the method 200 of a neural network
system implementing embodiments of the present approach to
preventing or avoiding sending improper messages or sensitive
information to receivers through electronic communication channels
can be implemented on a cloud computing platform, where the
analysis of electronic communications may be done on distributed
computer resources. The neural network can be implemented over
multiple nodes.
[0055] FIG. 3 is an exemplary processing system 300 to which the
present methods and systems may be applied, in accordance with an
embodiment of the present invention.
[0056] The processing system 300 can include at least one processor
(CPU) 304 and at least one graphics processing (GPU) 305 that can
perform vector calculations/manipulations operatively coupled to
other components via a system bus 302. A cache 306, a Read Only
Memory (ROM) 308, a Random Access Memory (RAM) 310, an input/output
(I/O) adapter 320, a sound adapter 330, a network adapter 340, a
user interface adapter 350, and a display adapter 360, can be
operatively coupled to the system bus 302.
[0057] A first storage device 322 and a second storage device 324
are operatively coupled to system bus 302 by the I/O adapter 320.
The storage devices 322 and 324 can be any of a disk storage device
(e.g., a magnetic or optical disk storage device), a solid state
magnetic device, and so forth. The storage devices 322 and 324 can
be the same type of storage device or different types of storage
devices.
[0058] A speaker 332 is operatively coupled to system bus 302 by
the sound adapter 330. A transceiver 342 is operatively coupled to
system bus 302 by network adapter 340. A display device 362 is
operatively coupled to system bus 302 by display adapter 360.
[0059] A first user input device 352, a second user input device
354, and a third user input device 356 are operatively coupled to
system bus 302 by user interface adapter 350. The user input
devices 352, 354, and 356 can be any of a keyboard, a mouse, a
keypad, an image capture device, a motion sensing device, a
microphone, a device incorporating the functionality of at least
two of the preceding devices, and so forth. Of course, other types
of input devices can also be used, while maintaining the spirit of
the present principles. The user input devices 352, 354, and 356
can be the same type of user input device or different types of
user input devices. The user input devices 352, 354, and 356 can be
used to input and output information to and from system 300.
[0060] In various embodiments, the processing system 300 may also
include other elements (not shown), as readily contemplated by one
of skill in the art, as well as omit certain elements. For example,
various other input devices and/or output devices can be included
in processing system 300, depending upon the particular
implementation of the same, as readily understood by one of
ordinary skill in the art. For example, various types of wireless
and/or wired input and/or output devices can be used. Moreover,
additional processors, controllers, memories, and so forth, in
various configurations can also be utilized as readily appreciated
by one of ordinary skill in the art. These and other variations of
the processing system 300 are readily contemplated by one of
ordinary skill in the art given the teachings of the present
principles provided herein.
[0061] Moreover, it is to be appreciated that system 300 is a
system for implementing respective embodiments of the present
methods/systems. Part or all of processing system 300 may be
implemented in one or more of the elements of FIGS. 1-2.
[0062] Further, it is to be appreciated that processing system 300
may perform at least part of the methods described herein
including, for example, at least part of method 100 of FIG. 1 and
method 200 of FIG. 2. Processing system 300 may be part of a cloud
computing platform.
[0063] FIG. 4 is a block diagram illustratively depicting an
exemplary processing system implementing a neural network for
interrupting objectionable electronic message transmissions, in
accordance with an embodiment of the present invention.
[0064] In one or more embodiments, the automated prevention of
sending objectionable electronic messages can be implemented as a
neural network system 400 on a processing system 300, where the
processing system can include a CPU 410, a GPU 420, a bus 430
electrically connected to and in electronic communication with the
CPU 410 and the GPU 420. The neural network system 400 can be
stored in the memory of processing system 300 and run by CPU 410
and/or GPU 420.
[0065] In one or more embodiments, a normalizer 440 can be
electrically connected to and in electronic communication with the
bus 430, where the normalizer 440 can normalize stored electronic
messages from a communication history, pre-existing electronic
messages of a message thread, and/or a newly created electronic
message (collectively referred to as "electronic message(s)") input
into the neural network system 400. Normalization can include
removing stop-words, correcting or removing mis-spellings and
typos, and/or enriching the electronic messages using context
information.
[0066] In one or more embodiments, a content extractor 450 can be
electrically connected to and in electronic communication with the
bus 430, where the content extractor 450 can extract content from
the electronic message(s) using key term identification and/or
LDA.
[0067] In one or more embodiments, a cluster generator 460 can be
electrically connected to and in electronic communication with the
bus 430, where the cluster generator 460 can generate clusters from
the content of the electronic message(s) using expectation
maximization (EM) approach, a K-means approach (e.g., using
MacQueen's algorithm, Hartigan's algorithm, etc.), or a Gaussian
Mixture Model.
[0068] In one or more embodiments, a message filterer 470 can be
electrically connected to and in electronic communication with the
bus 430, where the message filterer 470 can filter the electronic
messages with or without attachments using black lists, white
lists, and/or one or more filter matrices, F, M.sub.N, M.sub.P.
[0069] In one or more embodiments, a positive reply generator 480
can be electrically connected to and in electronic communication
with the bus 430, where the positive reply generator 480 can
generate one or more possible positive replies that are predicted
responses from the listed recipients of a newly created electronic
message.
[0070] In one or more embodiments, a negative reply generator 490
can be electrically connected to and in electronic communication
with the bus 430, where the negative reply generator 490 can
generate one or more possible negative replies that are predicted
responses from the listed recipients of a newly created electronic
message.
[0071] In various embodiments, the neural network system 400 can be
in electronic communication with a messaging application 499 that
can be running on the system 400, where the system can interrupt
the sending of a newly created electronic message if the message is
determined to contain content that would be objectionable to one or
more of the intended recipients listed in the addressee field of
the newly created electronic message. The user/sender may be
prompted, for example, through a pop-up window on the system, to
re-evaluate the message content, the list of addressees listed in
the recipient field or both depending on, for example, the message
content and/or the identified addressees listed in the recipient
field with or without their profile relationships.
[0072] The processing system 300 can implement the positively and
negatively trained neural network on a communications platform
running the messaging application 499, wherein the positively
and/or negatively trained neural network system 400 analyzes
electronic communications sent using the communications platform
and the newly created electronic message(s), and pre-emptively
blocks a newly created electronic message identified as containing
objectionable material based on the set of corresponding recipients
for the newly created electronic message.
[0073] FIG. 5 is a block diagram illustratively depicting an
exemplary neural network, in accordance with an embodiment of the
present invention.
[0074] ANNs demonstrate an ability to derive meaning from
complicated or imprecise data and can be used to extract patterns
and detect trends that are too complex to be detected by humans or
other computer-based systems. A neural network 500 may include a
plurality of neurons/nodes 501, and the nodes 501 may communicate
using one or more of a plurality of connections 508. The neural
network 500 may include a plurality of layers, including, for
example, one or more input layers 502, one or more hidden layers
504, and one or more output layers 506. In an embodiment, nodes 501
at each layer may be employed to apply any function (e.g., input
program, input data, etc.) to any previous layer to produce output,
and the hidden layer 504 may be employed to transform inputs from
the input layer (or any other layer) into output for nodes 501 at
different levels.
[0075] This represents a "feed-forward" computation, where
information propagates from input neurons 502 to the output neurons
506. Upon completion of a feed-forward computation, the output is
compared to a desired output available from training data. The
error relative to the training data is then processed in
"feed-back" computation, where the hidden neurons 504 and input
neurons 502 receive information regarding the error propagating
backward from the output neurons 506. Once the backward error
propagation has been completed, weight updates are performed, with
the weighted connections 508 being updated to account for the
received error.
[0076] FIG. 6 is a block diagram illustratively depicting an
exemplary artificial neural network (ANN) architecture 600, in
accordance with an embodiment of the present invention.
[0077] It should be understood that the present architecture 600 is
purely exemplary and that other architectures or types of neural
network may be used instead. During feed-forward operation, a set
of input neurons 602 each provide an input voltage in parallel to a
respective row of weights 604. The weights 604 each have a settable
resistance value, such that a current output flows from the weight
604 to a respective hidden neuron 606 to represent the weighted
input. The current output by a given weight is determined as I=V/r,
where V is the input voltage from the input neuron 602 and r is the
set resistance of the weight 604. The current from each weight adds
column-wise and flows to a hidden neuron 606. A set of reference
weights 607 have a fixed resistance and combine their outputs into
a reference current that is provided to each of the hidden neurons
606. Because conductance values can only be positive numbers, some
reference conductance is needed to encode both positive and
negative values in the matrix. The currents produced by the weights
604 are continuously valued and positive, and therefore the
reference weights 607 are used to provide a reference current,
above which currents are considered to have positive values and
below which currents are considered to have negative values.
[0078] As an alternative to using the reference weights 607,
another embodiment may use separate arrays of weights 604 to
capture negative values. Each approach has advantages and
disadvantages. Using the reference weights 607 is more efficient in
chip area, but reference values need to be matched closely to one
another. In contrast, the use of a separate array for negative
values does not involve close matching as each value has a pair of
weights to compare against. However, the negative weight matrix
approach uses roughly twice the chip area as compared to the single
reference weight column. In addition, the reference weight column
generates a current that needs to be copied to each neuron for
comparison, whereas a negative matrix array provides a reference
value directly for each neuron. In the negative array embodiment,
the weights 604 of both positive and negative arrays are updated,
but this also increases signal-to-noise ratio as each weight value
is a difference of two conductance values. The two embodiments
provide identical functionality in encoding a negative value and
those having ordinary skill in the art will be able to choose a
suitable embodiment for the application at hand.
[0079] The hidden neurons 606 use the currents from the array of
weights 604 and the reference weights 607 to perform some
calculation. The hidden neurons 606 then output a voltage of their
own to another array of weights 604. This array performs in the
same way, with a column of weights 604 receiving a voltage from
their respective hidden neuron 606 to produce a weighted current
output that adds row-wise and is provided to the output neuron
608.
[0080] It should be understood that any number of these stages may
be implemented, by interposing additional layers of arrays and
hidden neurons 606. It should also be noted that some neurons may
be constant neurons 609, which provide a constant voltage to the
array. The constant neurons 609 can be present among the input
neurons 602 and/or hidden neurons 606 and are only used during
feed-forward operation.
[0081] During back propagation, the output neurons 608 provide a
voltage back across the array of weights 604. The output layer
compares the generated network response to training data and
computes an error. The error is applied to the array as a voltage
pulse, where the height and/or duration of the pulse is modulated
proportional to the error value. In this example, a row of weights
604 receives a voltage from a respective output neuron 608 in
parallel and converts that voltage into a current which adds
column-wise to provide an input to hidden neurons 606. The hidden
neurons 606 combine the weighted feedback signal with a derivative
of its feed-forward calculation and stores an error value before
outputting a feedback signal voltage to its respective column of
weights 604. This back propagation travels through the entire
network 600 until all hidden neurons 606 and the input neurons 602
have stored an error value.
[0082] During weight updates, the input neurons 602 and hidden
neurons 606 apply a first weight update voltage forward and the
output neurons 608 and hidden neurons 606 apply a second weight
update voltage backward through the network 600. The combinations
of these voltages create a state change within each weight 604,
causing the weight 604 to take on a new resistance value. In this
manner the weights 604 can be trained to adapt the neural network
600 to errors in its processing. It should be noted that the three
modes of operation, feed forward, back propagation, and weight
update, do not overlap with one another.
[0083] FIG. 7 is an illustration of a cloud computing environment,
in accordance with an embodiment of the present invention.
[0084] It is to be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0085] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0086] Characteristics are as follows:
[0087] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0088] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0089] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0090] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0091] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported, providing
transparency for both the provider and consumer of the utilized
service.
[0092] Service Models are as follows:
[0093] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0094] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0095] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0096] Deployment Models are as follows:
[0097] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0098] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0099] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0100] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0101] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
[0102] As shown in FIG. 7, cloud computing environment 700 includes
one or more cloud computing nodes 710 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone or smartphone 720,
desktop computer 730, laptop computer 740, and/or automobile
computer system 750 can communicate. Nodes 710 can communicate with
one another through wired and/or wireless communication links that
form a network (e.g., Internet, WAN, LAN, etc.). The nodes 710 may
be grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described herein, or a combination thereof. This allows cloud
computing environment 700 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 720, 730, 740, 750, shown in
FIG. 7 are intended to be illustrative only and that computing
nodes 710 and cloud computing environment 700 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0103] FIG. 8 is an illustration of a set of functional abstraction
layers provided by a cloud computing environment 700, as shown in
FIG. 7, in accordance with an embodiment of the present
invention.
[0104] It should be understood in advance that the components,
layers, and functions shown in FIG. 8 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0105] Hardware and software layer 810 can include hardware and
software components. Examples of hardware components include:
mainframes 811; RISC (Reduced Instruction Set Computer)
architecture based servers 812; servers 813; blade servers 814;
storage devices 815; and networks and networking components 816. In
some embodiments, software components include network application
server software and database software.
[0106] Virtualization layer 820 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 821; virtual storage 822; virtual networks 823,
including virtual private networks; virtual applications and
operating systems 824; and virtual clients 825.
[0107] In one example, management layer 830 may provide the
functions described below. Resource provisioning 831 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 832 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security 833 provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 834 provides access to the cloud computing environment for
consumers and system administrators. Service level management 835
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 836 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0108] Workloads layer 840 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 841; software development and
lifecycle management 842; virtual classroom education delivery 843;
data analytics processing 844; and electronic communication and
electronic message analysis 845.
[0109] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0110] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0111] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0112] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as SMALLTALK, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0113] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0114] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0115] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0116] Reference in the specification to "one embodiment" or "an
embodiment" of the present invention, as well as other variations
thereof, means that a particular feature, structure,
characteristic, and so forth described in connection with the
embodiment is included in at least one embodiment of the present
invention. Thus, the appearances of the phrase "in one embodiment"
or "in an embodiment", as well any other variations, appearing in
various places throughout the specification are not necessarily all
referring to the same embodiment.
[0117] It is to be appreciated that the use of any of the following
"/", "and/or", and "at least one of", for example, in the cases of
"A/B", "A and/or B" and "at least one of A and B", is intended to
encompass the selection of the first listed option (A) only, or the
selection of the second listed option (B) only, or the selection of
both options (A and B). As a further example, in the cases of "A,
B, and/or C" and "at least one of A, B, and C", such phrasing is
intended to encompass the selection of the first listed option (A)
only, or the selection of the second listed option (B) only, or the
selection of the third listed option (C) only, or the selection of
the first and the second listed options (A and B) only, or the
selection of the first and third listed options (A and C) only, or
the selection of the second and third listed options (B and C)
only, or the selection of all three options (A and B and C). This
may be extended, as readily apparent by one of ordinary skill in
this and related arts, for as many items listed.
[0118] Having described preferred embodiments of a system and
method (which are intended to be illustrative and not limiting), it
is noted that modifications and variations can be made by persons
skilled in the art in light of the above teachings. It is therefore
to be understood that changes may be made in the particular
embodiments disclosed which are within the scope of the invention
as outlined by the appended claims. Having thus described aspects
of the invention, with the details and particularity required by
the patent laws, what is claimed and desired protected by Letters
Patent is set forth in the appended claims.
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